Senior Machine Learning Engineer, Hyderabad

Within Warner Bros. Discovery's SPARK organisation, the Machine Learning Engineering team turns first-party audience signals into ML capabilities for identity, audience intelligence, advertising, personalisation, forecasting, engagement and retention. Machine learning engineers at WBD combine rigorous data science with ownership of the engineering that brings models to life, including production ML data pipelines, model training and optimisation, and the ML infrastructure — feature stores, training and serving pipelines, and MLOps — that makes the work reliable, repeatable and scalable.

The team builds primarily on Databricks, with strong working knowledge of Snowflake and AWS, and is an early, enthusiastic adopter of agentic AI development workflows. The position carries senior technical leadership, mentorship and cross-functional partnership responsibilities across global markets.

• Design and operate low-latency online serving systems for fraud scoring, message decisioning and real-time personalisation.
• Build ML models for identity resolution, audience intelligence, content affinity modelling, genre-preference modelling and time-series forecasting across global markets.
• Integrate with personalisation systems to consume in-app user signals and design feature pipelines that fuse real-time streaming signals with batch-computed features for online scoring.
• Partner with Product, Engineering and Data Science to translate business problems into well-scoped ML solutions.
• Evaluate new technologies and approaches, including DCR-native modelling, graph ML, agentic ML orchestration and LLM-augmented pipelines, with clear build/buy/partner recommendations.
• Architect probabilistic identity resolution systems that connect unauthenticated device IDs and first-party cookies to households or persons with calibrated confidence across WBD brands.
• Lead the evolution of Audience Intelligence, including ML Promo Optimizer, STAT v2, lookalike modelling inside Snowflake DCR, and content segmentation.
• Own ML architecture for forecasting use cases such as audience growth, demand, yield and pricing, ensuring models are monitored and continuously improved.
• Bring ML personalisation signals, such as genre/content affinity and engagement trends, into batch and future real-time activation paths.
• Design offline and online evaluation approaches with clear baselines, success metrics, experiment design and promotion criteria, and improve feature and label quality, leakage prevention, bias checks, calibration, explainability and impact measurement.
• Identify technical risks early, document tradeoffs and drive mitigation plans with engineering, data, product and business partners, turning incidents and postmortems into reusable standards, automated checks and platform improvements.
• Mentor MLE 2 engineers through architecture reviews, design discussions, implementation guidance and hands‑on problem solving, and partner with the ML leads on technical roadmap shaping, execution planning and capability development.
• Represent the team in technical forums with ML, Data Engineering, Product, Ad Sales and US-based stakeholders, and create clear design docs, architecture reviews, readiness reviews and postmortems.

• 5–8 years of ML engineering experience, or 2+ years with a PhD, with demonstrated senior-level scope, technical ownership and cross-team impact.
• Comfortable owning systems end-to-end, from problem definition through serving and monitoring, not just the modelling layer.
• Experience working on optimisation problems such as ranking, personalisation or multi-objective decisioning in environments with interacting metrics.
• Hands‑on experience with Databricks, Spark and…